Computationally efficient model selection

ABSTRACT

In various implementations, a device surveys a scene and presents, within the scene, a extended reality (XR) environment including one or more assets that evolve over time (e.g., change location or age). Modeling such an XR environment at various timescales can be computationally intensive, particularly when modeling the XR environment over larger timescales. Accordingly, in various implementations, different models are used to determine the environment state of the XR environment when presenting the XR environment at different timescales.

CROSS-REFERENCES TO RELATED APPLICATIONS

This application is a continuation of Intl. Patent App. No.PCT/US2019/052587, filed on Sep. 24, 2019, which claims priority to U.S.Provisional Patent App. No. 62/738,066, filed on Sep. 28, 2019, whichare both hereby incorporated by reference in their entirety.

TECHNICAL FIELD

The present disclosure generally relates to generating an extendedreality (XR) environment, and in particular, to systems, methods, anddevices for determining environment states for an XR environment in acomputationally efficient manner.

BACKGROUND

To present an XR environment that includes assets that evolve over timeaccording to one or more models, extensive computation may be needed.

BRIEF DESCRIPTION OF THE DRAWINGS

So that the present disclosure can be understood by those of ordinaryskill in the art, a more detailed description may be had by reference toaspects of some illustrative implementations, some of which are shown inthe accompanying drawings.

FIG. 1 is a block diagram of an example operating architecture inaccordance with some implementations.

FIG. 2 is a block diagram of an example controller in accordance withsome implementations.

FIG. 3 is a block diagram of an example electronic device in accordancewith some implementations.

FIG. 4 illustrates a scene with an electronic device surveying thescene.

FIGS. 5A-5I illustrates a portion of the display of the electronicdevice of FIG. 4 displaying images of a representation of the sceneincluding an XR environment.

FIG. 6 illustrates an environment state in accordance with someimplementations.

FIG. 7 illustrates a block diagram of an environment state generator inaccordance with some implementations.

FIG. 8 is a flowchart representation of a method of generating anenvironment state of an XR environment in accordance with someimplementations.

FIG. 9 is a flowchart representation of a method of generatingenvironment states of an XR environment in accordance with someimplementations.

In accordance with common practice the various features illustrated inthe drawings may not be drawn to scale. Accordingly, the dimensions ofthe various features may be arbitrarily expanded or reduced for clarity.In addition, some of the drawings may not depict all of the componentsof a given system, method or device. Finally, like reference numeralsmay be used to denote like features throughout the specification andfigures.

SUMMARY

Various implementations disclosed herein include devices, systems, andmethods for generating environment states of an environment. In variousimplementations, the method is performed at a device including one ormore processors and non-transitory memory. The method includes obtaininga first environment state, associated with a first environment time, ofan environment, wherein the first environment state indicates inclusionin the environment of one or more assets and further indicates one ormore states of the one or more assets. The method includes determining,according to a first model and based on the first environment state, asecond environment state associated with a second environment time. Themethod includes receiving an input indicative of a timestep from thesecond environment time to a third environment time, wherein thetimestep is different than a difference between the first environmenttime and the second environment time. The method includes determining,according to a second model, different than the first model, and basedon the second environment state, a third environment state associatedwith the third environment time.

Various implementations disclosed herein include devices, systems, andmethods for generating an environment state of an environment accordingto a selected model. In various implementations, the method is performedat a device including one or more processors and non-transitory memory.The method includes obtaining a first environment state, associated witha first environment time, of an environment, wherein the firstenvironment state indicates inclusion in the environment of one or moreassets and further indicates one or more states of the one or moreassets. The method includes determining a timestep from the firstenvironment time to a second environment time. The method includesselecting, based on the timestep, a model from a plurality of differentmodels. The method includes determining, in accordance with the selectedmodel, a second environment state, associated with the secondenvironment time, of the XR environment.

In accordance with some implementations, a device includes one or moreprocessors, a non-transitory memory, and one or more programs; the oneor more programs are stored in the non-transitory memory and configuredto be executed by the one or more processors and the one or moreprograms include instructions for performing or causing performance ofany of the methods described herein. In accordance with someimplementations, a non-transitory computer readable storage medium hasstored therein instructions, which, when executed by one or moreprocessors of a device, cause the device to perform or cause performanceof any of the methods described herein. In accordance with someimplementations, a device includes: one or more processors, anon-transitory memory, and means for performing or causing performanceof any of the methods described herein.

DESCRIPTION

A physical environment refers to a physical place that people can senseand/or interact with without aid of electronic devices. The physicalenvironment may include physical features such as a physical surface ora physical object. For example, the physical environment corresponds toa physical park that includes physical trees, physical buildings, andphysical people. People can directly sense and/or interact with thephysical environment such as through sight, touch, hearing, taste, andsmell. In contrast, an extended reality (XR) environment refers to awholly or partially simulated environment that people sense and/orinteract with via an electronic device. For example, the XR environmentmay include augmented reality (AR) content, mixed reality (MR) content,virtual reality (VR) content, and/or the like. With an XR system, asubset of a person's physical motions, or representations thereof, aretracked, and, in response, one or more characteristics of one or morevirtual objects simulated in the XR environment are adjusted in a mannerthat comports with at least one law of physics. As an example, the XRsystem may detect movement of the electronic device presenting the XRenvironment (e.g., a mobile phone, a tablet, a laptop, a head-mounteddevice, and/or the like) and, in response, adjust graphical content andan acoustic field presented by the electronic device to the person in amanner similar to how such views and sounds would change in a physicalenvironment. In some situations (e.g., for accessibility reasons), theXR system may adjust characteristic(s) of graphical content in the XRenvironment in response to representations of physical motions (e.g.,vocal commands).

There are many different types of electronic systems that enable aperson to sense and/or interact with various XR environments. Examplesinclude head-mountable systems, projection-based systems, heads-updisplays (HUDs), vehicle windshields having integrated displaycapability, windows having integrated display capability, displaysformed as lenses designed to be placed on a person's eyes (e.g., similarto contact lenses), headphones/earphones, speaker arrays, input systems(e.g., wearable or handheld controllers with or without hapticfeedback), smartphones, tablets, and desktop/laptop computers. Ahead-mountable system may have one or more speaker(s) and an integratedopaque display. Alternatively, a head-mountable system may be configuredto accept an external opaque display (e.g., a smartphone). Thehead-mountable system may incorporate one or more imaging sensors tocapture images or video of the physical environment, and/or one or moremicrophones to capture audio of the physical environment. Rather than anopaque display, a head-mountable system may have a transparent ortranslucent display. The transparent or translucent display may have amedium through which light representative of images is directed to aperson's eyes. The display may utilize digital light projection, OLEDs,LEDs, uLEDs, liquid crystal on silicon, laser scanning light sources, orany combination of these technologies. The medium may be an opticalwaveguide, a hologram medium, an optical combiner, an optical reflector,or any combination thereof. In some implementations, the transparent ortranslucent display may be configured to become opaque selectively.Projection-based systems may employ retinal projection technology thatprojects graphical images onto a person's retina. Projection systemsalso may be configured to project virtual objects into the physicalenvironment, for example, as a hologram or on a physical surface.

Numerous details are described in order to provide a thoroughunderstanding of the example implementations shown in the drawings.However, the drawings merely show some example aspects of the presentdisclosure and are therefore not to be considered limiting. Those ofordinary skill in the art will appreciate that other effective aspectsand/or variants do not include all of the specific details describedherein. Moreover, well-known systems, methods, components, devices andcircuits have not been described in exhaustive detail so as not toobscure more pertinent aspects of the example implementations describedherein.

In various implementations, a device surveys a scene and presents,within the scene, an XR environment including one or more assets thatevolve over time (e.g., change location or age). Modeling such an XRenvironment at various timescales can be computationally intensive,particularly when modeling the XR environment over larger timescales.Accordingly, in various implementations, different models are used todetermine the environment state of the XR environment when presentingthe XR environment at different timescales.

FIG. 1 is a block diagram of an example operating environment 100 inaccordance with some implementations. While pertinent features areshown, those of ordinary skill in the art will appreciate from thepresent disclosure that various other features have not been illustratedfor the sake of brevity and so as not to obscure more pertinent aspectsof the example implementations disclosed herein. To that end, as anon-limiting example, the operating environment 100 includes acontroller 110 and an electronic device 120.

In some implementations, the controller 110 is configured to manage andcoordinate an XR experience for the user. In some implementations, thecontroller 110 includes a suitable combination of software, firmware,and/or hardware. The controller 110 is described in greater detail belowwith respect to FIG. 2. In some implementations, the controller 110 is acomputing device that is local or remote relative to the physicalenvironment 105. For example, the controller 110 is a local serverlocated within the physical environment 105. In another example, thecontroller 110 is a remote server located outside of the physicalenvironment 105 (e.g., a cloud server, central server, etc.). In someimplementations, the controller 110 is communicatively coupled with theelectronic device 120 via one or more wired or wireless communicationchannels 144 (e.g., BLUETOOTH, IEEE 802.11x, IEEE 802.16x, IEEE 802.3x,etc.). In another example, the controller 110 is included within theenclosure of the electronic device 120. In some implementations, thefunctionalities of the controller 110 are provided by and/or combinedwith the electronic device 120.

In some implementations, the electronic device 120 is configured toprovide the XR experience to the user. In some implementations, theelectronic device 120 includes a suitable combination of software,firmware, and/or hardware. According to some implementations, theelectronic device 120 presents, via a display 122, XR content to theuser while the user is physically present within the physicalenvironment 105 that includes a table 107 within the field-of-view 111of the electronic device 120. As such, in some implementations, the userholds the electronic device 120 in his/her hand(s). In someimplementations, while providing XR content, the electronic device 120is configured to display an XR object (e.g., an XR cylinder 109) and toenable video pass-through of the physical environment 105 (e.g.,including a representation 117 of the table 107) on a display 122. Theelectronic device 120 is described in greater detail below with respectto FIG. 3.

According to some implementations, the electronic device 120 provides anXR experience to the user while the user is virtually and/or physicallypresent within the physical environment 105.

In some implementations, the user wears the electronic device 120 onhis/her head. For example, in some implementations, the electronicdevice includes a head-mounted system (HMS), head-mounted device (HMD),or head-mounted enclosure (HME). As such, the electronic device 120includes one or more XR displays provided to display the XR content. Forexample, in various implementations, the electronic device 120 enclosesthe field-of-view of the user. In some implementations, the electronicdevice 120 is a handheld device (such as a smartphone or tablet)configured to present XR content, and rather than wearing the electronicdevice 120, the user holds the device with a display directed towardsthe field-of-view of the user and a camera directed towards the physicalenvironment 105. In some implementations, the handheld device can beplaced within an enclosure that can be worn on the head of the user. Insome implementations, the electronic device 120 is replaced with an XRchamber, enclosure, or room configured to present XR content in whichthe user does not wear or hold the electronic device 120.

FIG. 2 is a block diagram of an example of the controller 110 inaccordance with some implementations. While certain specific featuresare illustrated, those skilled in the art will appreciate from thepresent disclosure that various other features have not been illustratedfor the sake of brevity, and so as not to obscure more pertinent aspectsof the implementations disclosed herein. To that end, as a non-limitingexample, in some implementations the controller 110 includes one or moreprocessing units 202 (e.g., microprocessors, application-specificintegrated-circuits (ASICs), field-programmable gate arrays (FPGAs),graphics processing units (GPUs), central processing units (CPUs),processing cores, and/or the like), one or more input/output (I/O)devices 206, one or more communication interfaces 208 (e.g., universalserial bus (USB), FIREWIRE, THUNDERBOLT, IEEE 802.3x, IEEE 802.11x, IEEE802.16x, global system for mobile communications (GSM), code divisionmultiple access (CDMA), time division multiple access (TDMA), globalpositioning system (GPS), infrared (IR), BLUETOOTH, ZIGBEE, and/or thelike type interface), one or more programming (e.g., I/O) interfaces210, a memory 220, and one or more communication buses 204 forinterconnecting these and various other components.

In some implementations, the one or more communication buses 204 includecircuitry that interconnects and controls communications between systemcomponents. In some implementations, the one or more I/O devices 206include at least one of a keyboard, a mouse, a touchpad, a joystick, oneor more microphones, one or more speakers, one or more image sensors,one or more displays, and/or the like.

The memory 220 includes high-speed random-access memory, such as dynamicrandom-access memory (DRAM), static random-access memory (SRAM),double-data-rate random-access memory (DDR RAM), or other random-accesssolid-state memory devices. In some implementations, the memory 220includes non-volatile memory, such as one or more magnetic disk storagedevices, optical disk storage devices, flash memory devices, or othernon-volatile solid-state storage devices. The memory 220 optionallyincludes one or more storage devices remotely located from the one ormore processing units 202. The memory 220 comprises a non-transitorycomputer readable storage medium. In some implementations, the memory220 or the non-transitory computer readable storage medium of the memory220 stores the following programs, modules and data structures, or asubset thereof including an optional operating system 230 and an XRexperience module 240.

The operating system 230 includes procedures for handling various basicsystem services and for performing hardware dependent tasks. In someimplementations, the XR experience module 240 is configured to manageand coordinate one or more XR experiences for one or more users (e.g., asingle XR experience for one or more users, or multiple XR experiencesfor respective groups of one or more users). To that end, in variousimplementations, the XR experience module 240 includes a data obtainingunit 242, a tracking unit 244, a coordination unit 246, and a datatransmitting unit 248.

In some implementations, the data obtaining unit 242 is configured toobtain data (e.g., presentation data, interaction data, sensor data,location data, etc.) from at least the electronic device 120 of FIG. 1.To that end, in various implementations, the data obtaining unit 242includes instructions and/or logic therefor, and heuristics and metadatatherefor.

In some implementations, the tracking unit 244 is configured to map thephysical environment 105 and to track the position/location of at leastthe electronic device 120 with respect to the physical environment 105of FIG. 1. To that end, in various implementations, the tracking unit244 includes instructions and/or logic therefor, and heuristics andmetadata therefor.

In some implementations, the coordination unit 246 is configured tomanage and coordinate the XR experience presented to the user by theelectronic device 120. To that end, in various implementations, thecoordination unit 246 includes instructions and/or logic therefor, andheuristics and metadata therefor.

In some implementations, the data transmitting unit 248 is configured totransmit data (e.g., presentation data, location data, etc.) to at leastthe electronic device 120. To that end, in various implementations, thedata transmitting unit 248 includes instructions and/or logic therefor,and heuristics and metadata therefor.

Although the data obtaining unit 242, the tracking unit 244, thecoordination unit 246, and the data transmitting unit 248 are shown asresiding on a single device (e.g., the controller 110), it should beunderstood that in other implementations, any combination of the dataobtaining unit 242, the tracking unit 244, the coordination unit 246,and the data transmitting unit 248 may be located in separate computingdevices.

Moreover, FIG. 2 is intended more as functional description of thevarious features that may be present in a particular implementation asopposed to a structural schematic of the implementations describedherein. As recognized by those of ordinary skill in the art, items shownseparately could be combined and some items could be separated. Forexample, some functional modules shown separately in FIG. 2 could beimplemented in a single module and the various functions of singlefunctional blocks could be implemented by one or more functional blocksin various implementations. The actual number of modules and thedivision of particular functions and how features are allocated amongthem will vary from one implementation to another and, in someimplementations, depends in part on the particular combination ofhardware, software, and/or firmware chosen for a particularimplementation.

FIG. 3 is a block diagram of an example of the electronic device 120 inaccordance with some implementations. While certain specific featuresare illustrated, those skilled in the art will appreciate from thepresent disclosure that various other features have not been illustratedfor the sake of brevity, and so as not to obscure more pertinent aspectsof the implementations disclosed herein. To that end, as a non-limitingexample, in some implementations the electronic device 120 includes oneor more processing units 302 (e.g., microprocessors, ASICs, FPGAs, GPUs,CPUs, processing cores, and/or the like), one or more input/output (I/O)devices and sensors 306, one or more communication interfaces 308 (e.g.,USB, FIREWIRE, THUNDERBOLT, IEEE 802.3x, IEEE 802.11x, IEEE 802.16x,GSM, CDMA, TDMA, GPS, IR, BLUETOOTH, ZIGBEE, and/or the like typeinterface), one or more programming (e.g., I/O) interfaces 310, one ormore XR displays 312, one or more optional interior- and/orexterior-facing image sensors 314, a memory 320, and one or morecommunication buses 304 for interconnecting these and various othercomponents.

In some implementations, the one or more communication buses 304 includecircuitry that interconnects and controls communications between systemcomponents. In some implementations, the one or more I/O devices andsensors 306 include at least one of an inertial measurement unit (IMU),an accelerometer, a gyroscope, a thermometer, one or more physiologicalsensors (e.g., blood pressure monitor, heart rate monitor, blood oxygensensor, blood glucose sensor, etc.), one or more microphones, one ormore speakers, a haptics engine, one or more depth sensors (e.g., astructured light, a time-of-flight, or the like), and/or the like.

In some implementations, the one or more XR displays 312 are configuredto provide the XR experience to the user. In some implementations, theone or more XR displays 312 correspond to holographic, digital lightprocessing (DLP), liquid-crystal display (LCD), liquid-crystal onsilicon (LCoS), organic light-emitting field-effect transitory (OLET),organic light-emitting diode (OLED), surface-conduction electron-emitterdisplay (SED), field-emission display (FED), quantum-dot light-emittingdiode (QD-LED), micro-electro-mechanical system (MEMS), and/or the likedisplay types. In some implementations, the one or more XR displays 312correspond to diffractive, reflective, polarized, holographic, etc.waveguide displays. For example, the electronic device 120 includes asingle XR display. In another example, the electronic device includes anXR display for each eye of the user. In some implementations, the one ormore XR displays 312 are capable of presenting MR and VR content.

In some implementations, the one or more image sensors 314 areconfigured to obtain image data that corresponds to at least a portionof the face of the user that includes the eyes of the user (any may bereferred to as an eye-tracking camera). In some implementations, the oneor more image sensors 314 are configured to be forward-facing so as toobtain image data that corresponds to the scene as would be viewed bythe user if the electronic device 120 was not present (and may bereferred to as a scene camera). The one or more optional image sensors314 can include one or more RGB cameras (e.g., with a complimentarymetal-oxide-semiconductor (CMOS) image sensor or a charge-coupled device(CCD) image sensor), one or more infrared (IR) cameras, one or moreevent-based cameras, and/or the like.

The memory 320 includes high-speed random-access memory, such as DRAM,SRAM, DDR RAM, or other random-access solid-state memory devices. Insome implementations, the memory 320 includes non-volatile memory, suchas one or more magnetic disk storage devices, optical disk storagedevices, flash memory devices, or other non-volatile solid-state storagedevices. The memory 320 optionally includes one or more storage devicesremotely located from the one or more processing units 302. The memory320 comprises a non-transitory computer readable storage medium. In someimplementations, the memory 320 or the non-transitory computer readablestorage medium of the memory 320 stores the following programs, modulesand data structures, or a subset thereof including an optional operatingsystem 330 and an XR presentation module 340.

The operating system 330 includes procedures for handling various basicsystem services and for performing hardware dependent tasks. In someimplementations, the XR presentation module 340 is configured to presentXR content to the user via the one or more XR displays 312. To that end,in various implementations, the XR presentation module 340 includes adata obtaining unit 342, an XR presenting unit 344, an XR environmentunit 346, and a data transmitting unit 348.

In some implementations, the data obtaining unit 342 is configured toobtain data (e.g., presentation data, interaction data, sensor data,location data, etc.) from at least the controller 110 of FIG. 1. To thatend, in various implementations, the data obtaining unit 342 includesinstructions and/or logic therefor, and heuristics and metadatatherefor.

In some implementations, the XR presenting unit 344 is configured topresent XR content via the one or more XR displays 312. To that end, invarious implementations, the XR presenting unit 344 includesinstructions and/or logic therefor, and heuristics and metadatatherefor.

In some implementations, the XR environment unit 346 is configured togenerate one or more environment states of an XR environment. To thatend, in various implementations, the XR environment unit 346 includesinstructions and/or logic therefor, and heuristics and metadatatherefor.

In some implementations, the data transmitting unit 348 is configured totransmit data (e.g., presentation data, location data, etc.) to at leastthe controller 110. In some implementations, the data transmitting unit348 is configured to transmit the request for a content rendering. Tothat end, in various implementations, the data transmitting unit 348includes instructions and/or logic therefor, and heuristics and metadatatherefor.

Although the data obtaining unit 342, the XR presenting unit 344, the XRenvironment unit 346, and the data transmitting unit 348 are shown asresiding on a single device (e.g., the electronic device 120), it shouldbe understood that in other implementations, any combination of the dataobtaining unit 342, the XR presenting unit 344, the XR environment unit346, and the data transmitting unit 348 may be located in separatecomputing devices.

Moreover, FIG. 3 is intended more as a functional description of thevarious features that could be present in a particular implementation asopposed to a structural schematic of the implementations describedherein. As recognized by those of ordinary skill in the art, items shownseparately could be combined and some items could be separated. Forexample, some functional modules shown separately in FIG. 3 could beimplemented in a single module and the various functions of singlefunctional blocks could be implemented by one or more functional blocksin various implementations. The actual number of modules and thedivision of particular functions and how features are allocated amongthem will vary from one implementation to another and, in someimplementations, depends in part on the particular combination ofhardware, software, and/or firmware chosen for a particularimplementation.

FIG. 4 illustrates a scene 405 with an electronic device 410 surveyingthe scene 405. The scene 405 includes a table 408 and a wall 407.

The electronic device 410 displays, on a display, a representation ofthe scene 415 including a representation of the table 418 and arepresentation of the wall 417. In various implementations, therepresentation of the scene 415 is generated based on an image of thescene captured with a scene camera of the electronic device 410 having afield-of-view directed toward the scene 405. The representation of thescene 415 further includes an XR environment 409 displayed on therepresentation of the table 418.

As the electronic device 410 moves about the scene 405, therepresentation of the scene 415 changes in accordance with the change inperspective of the electronic device 410. Further, the XR environment409 correspondingly changes in accordance with the change in perspectiveof the electronic device 410. Accordingly, as the electronic device 410moves, the XR environment 409 appears in a fixed relationship withrespect to the representation of the table 418.

FIG. 5A illustrates a portion of the display of the electronic device410 displaying a first image 500A of the representation of the scene 415including the XR environment 409. In FIG. 5A, the XR environment 409 isdefined by a first environment state and is associated with a firstenvironment time (e.g., 1). The first environment state indicates theinclusion in the XR environment 409 of one or more assets and furtherindicates one or more states of the one or more assets. In variousimplementations, the environment state is a data object, such as an XMLfile.

Accordingly, the XR environment 409 displayed in the first image 500Aincludes a plurality of assets as defined by the first environmentstate. In FIG. 5A, the XR environment 409 includes a first tree 511having a first height 591 and a second tree 512 having a second height592. The XR environment 409 includes a first squirrel 521 at a firstlocation 581 and a second squirrel 522 at a second location 582. The XRenvironment includes a first acorn 531.

The first environment state indicates the inclusion of the first tree511 and defines one or more states of the first tree 511. For example,the first environment state indicates a first age of the first tree 511and a first location of the first tree 511. The first environment stateindicates the inclusion of the second tree 512 and defines one or morestates of the second tree 512. For example, the first environment stateindicates a first age of the second tree 512 and a first location of thesecond tree 512.

The first environment state indicates the inclusion of the firstsquirrel 521 and defines one or more states of the first squirrel 521.For example, the first environment state indicates a first age of thefirst squirrel 521, a first location of the first squirrel 521, and afirst motion vector of the first squirrel 521 indicating that the firstsquirrel 521 is moving toward the first acorn 531. The first environmentstate indicates the inclusion of the second squirrel 522 and defines oneor more states of the second squirrel 522. For example, the firstenvironment state indicates a first age of the second squirrel 522, afirst location of the second squirrel 522, and a first motion vector ofthe second squirrel 522 indicating the that second squirrel 522 ismoving toward the second tree 512.

The first environment state indicates the inclusion of the first acorn531 and defines one or more states of the first acorn 531. For example,the first environment state indicates a first location of the firstacorn 531 and a first held state of the first acorn 531 indicating thatthe first acorn 531 is not held by a squirrel.

The first image 500A further includes a time indicator 540 and aplurality of timescale affordances 551-554. The plurality of timescaleaffordances 551-554 includes a pause affordance 551, a play affordance552, a quick-play affordance 553, and a quicker-play affordance 554. InFIG. 5A, the time indicator 540 indicates a current time of the XRenvironment 409 of 1. Further, the pause affordance 551 is currentlyselected (as indicated by the different manner of display).

FIG. 5B illustrates a portion of the display of the electronic device410 displaying a second image 500B of the representation of the scene415 including the XR environment 409 in response to a user selection ofthe play affordance 552 and after a frame time (e.g., the inverse of thedisplay frame rate). In FIG. 5B, the time indicator 540 indicates acurrent time of the XR environment 409 of 2 (e.g., a first timestep of 1as compared to FIG. 5A). In FIG. 5B, the play affordance 552 iscurrently selected (as indicated by the different manner of display).

In FIG. 5B, the XR environment 409 is defined by a second environmentstate and is associated with a second environment time (e.g., 2). Invarious implementations, the second environment state is generatedaccording to a first model and based on the first environment state.

In various implementations, determining the second environment stateaccording to the first model includes determining a second age of thefirst tree 511 by adding the first timestep (e.g., 1) to the first ageof the first tree 511, determining a second age of the second tree 512by adding the first timestep to the first age of the second tree 512,determining a second age of the first squirrel 521 by adding the firsttimestep to the first age of the first squirrel 521, and determining asecond age of the second squirrel 522 by adding the first timestep tothe first age of the second squirrel 522.

In various implementations, determining the second environment stateaccording to the first model includes determining a second location ofthe first tree 511 by copying the first location of the first tree 511and determining a second location of the second tree 512 by copying thefirst location of the second tree 512. Thus, the first model indicatesthat the first tree 511 and second tree 512 (e.g., assets having anasset type of “TREE”) do not change location.

In various implementations, determining the second environment stateaccording to the first model includes determining a second location ofthe first squirrel 521 by adjusting the first location of the firstsquirrel 521 according to the first motion vector of the first squirrel521 and determining a second location of the second squirrel 522 byadjusting the first location of the second squirrel 522 according to thefirst motion vector of the second squirrel 522. Thus, the first modelindicates that the first squirrel 521 and second squirrel 522 (e.g.,assets having an asset type of “ANIMAL”) change location according to amotion vector.

In various implementations, determining the second environment stateaccording to the first model includes determining a second motion vectorof the first squirrel 521 based on the proximity of other assets to thesecond location of the first squirrel 521 and determining a secondmotion vector of the second squirrel 522 based on the proximity of otherassets to the second location of the second squirrel 522. For example,the first model indicates that the first squirrel 521 and secondsquirrel 522 (e.g., assets having an asset type of “ANIMAL” and an assetsub-type of “SQUIRREL”) have motion vectors which direct them to nearbyassets (e.g., trees, acorns, or other squirrels).

In various implementations, determining the second environment stateincludes determining a second location of the first acorn 531 based onthe first location of the first acorn 531 and the first held state ofthe first acorn 531. For example, the first model indicates that thefirst acorn 531 (e.g., assets having an asset type of “ACORN”) does notchange location when the held state indicates that the first acorn 531is not held, but changes in accordance with a change in location of anasset (e.g., a squirrel) that is holding the first acorn 531.

In various implementations, determining the second environment stateincludes determining a second held state of the first acorn 531 based onthe second location of the first acorn 531 and the second location ofthe first squirrel 521 and the second location of the second squirrel522. For example, the first model indicates that the first acorn 531(e.g., assets having an asset type of “ACORN”) changes its held state toindicate that it is being held by a particular asset having an assettype of “ANIMAL” and an asset sub-type of “SQUIRREL” when thatparticular asset is at the same location as the first acorn 531.

Accordingly, in FIG. 5B, as compared to FIG. 5A, the first squirrel 521has moved to a third location 583 closer to the first acorn 531 and thesecond squirrel 522 has moved location closer to the second tree 512.

FIG. 5C illustrates a portion of the display of the electronic device410 displaying a third image 500C of the representation of the scene 415including the XR environment 409 after another frame time. In FIG. 5C,the time indicator 540 indicates a current time of the XR environment409 of 3 (e.g., the first timestep of 1 as compared to FIG. 5B). In FIG.5C, the play affordance 552 remains selected (as indicated by thedifferent manner of display).

In FIG. 5C, the XR environment 409 is defined by a third environmentstate and is associated with a third environment time. In variousimplementations, the third environment state is generated according tothe first model and based on the second environment state.

In various implementations, determining the third environment stateaccording to the first model includes determining a third age of thefirst tree 511 by adding the first timestep (e.g., 1) to the second ageof the first tree 511, determining a third age of the second tree 512 byadding the first timestep to the second age of the second tree 512,determining a third age of the first squirrel 521 by adding the firsttimestep to the second age of the first squirrel 521, and determining athird age of the second squirrel 522 by adding the first timestep to thesecond age of the second squirrel 522.

In various implementations, determining the third environment stateaccording to the first model includes determining a second location ofthe first tree 511 by copying the second location of the first tree 511and determining a third location of the second tree 512 by copying thesecond location of the second tree 512. Thus, the first model indicatesthat the first tree 511 and second tree 512 (e.g., assets having anasset type of “TREE”) do not change location.

In various implementations, determining the third environment stateaccording to the first model includes determining a third location ofthe first squirrel 521 by adjusting the second location of the firstsquirrel 521 according to the second motion vector of the first squirrel521 and determining a third location of the second squirrel 522 byadjusting the second location of the second squirrel 522 according tothe second motion vector of the second squirrel 522. Thus, the firstmodel indicates that the first squirrel 521 and second squirrel 522(e.g., assets having an asset type of “ANIMAL”) change locationaccording to a motion vector.

In various implementations, determining the third environment stateaccording to the first model includes determining a third motion vectorof the first squirrel 521 based on the proximity of other assets to thethird location of the first squirrel 521 and determining a third motionvector of the second squirrel 522 based on the proximity of other assetsto the third location of the second squirrel 522. For example, the firstmodel indicates that the first squirrel 521 and second squirrel 522(e.g., assets having an asset type of “ANIMAL” and an asset sub-type of“SQUIRREL”) have motion vectors which direct them to nearby assets(e.g., trees, acorns, or other squirrels).

In various implementations, determining the third environment stateincludes determining a third location of the first acorn 531 based onthe second location of the first acorn 531 and the second held state ofthe first acorn 531. For example, the first model indicates that thefirst acorn 531 (e.g., assets having an asset type of “ACORN”) does notchange location when the held state indicates that the first acorn 531is not held, but changes in accordance with a change in location of anasset (e.g., a squirrel) that is holding the first acorn 531.

In various implementations, determining the third environment stateincludes determining a third held state of the first acorn 531 based onthe third location of the first acorn 531 and the third location of thefirst squirrel 521 and the third location of the second squirrel 522.Thus, the first model indicates that the first acorn 531 (e.g., assetshaving an asset type of “ACORN”) changes its held state to indicate thatit is being held by a particular asset having an asset type of “ANIMAL”and an asset sub-type of “SQUIRREL” when that particular asset is at thesame location as the first acorn 531.

In various implementations, determining the third environment stateincludes determining that an asset spawns a new asset. For example, invarious implementations, the first model indicates that assets having anasset type of “TREE” have a probability (which may be based on thecurrent age of the asset) of spawning an asset having an asset type of“ACORN”.

In various implementations, determining the third environment stateincludes determining that an asset expires. For example, in variousimplementations, the first model indicates that assets having an assettype of “SQUIRREL” expire when the age of the asset reaches a threshold.As another example, in various implementations, the first modelindicates that assets having an asset type of “TREE” have a probability(which may be based on the current age of the asset) of expiring.

In FIG. 5C, as compared to FIG. 5B, the first squirrel 521 has movedlocation to a fifth location 585 of the first acorn 531 and the secondsquirrel 522 has moved to a sixth location even closer to the secondtree 512. Further the XR environment 409 includes a new asset, a secondacorn 532, generated by the second tree 512.

FIG. 5D illustrates a portion of the display of the electronic device410 displaying a fourth image 500D of the representation of the scene415 including the XR environment 409 after another frame time. In FIG.5D, the time indicator 540 indicates a current time of the XRenvironment 409 of 4 (e.g., a timestep of 1 as compared to FIG. 5C). InFIG. 5D, the play affordance 552 remains selected (as indicated by thedifferent manner of display).

In FIG. 5D, the XR environment 409 is defined by a fourth environmentstate and is associated with a fourth environment time. In variousimplementations, the fourth environment state is generated according tothe first model and based on the third environment state. In variousimplementations, determining the fourth environment state according tothe first model and based on the third environment state is performed asdescribed above with respect to determining the third environment stateaccording to the first model and based on the second environment state.

In FIG. 5D, as compared to FIG. 5C, the first squirrel 521 (holding thefirst acorn 531) has moved location further from the first tree 511 andthe second squirrel 522 has moved to a seventh location 587 closer tothe second acorn 532.

FIG. 5E illustrates a portion of the display of the electronic device410 displaying a fifth image 500E of the representation of the scene 415including the XR environment 409 in response to a user selection of thequick-play affordance 553 and after a frame time. In FIG. 5E, the timeindicator 540 indicates a current time of the XR environment 409 of1,000,004 (e.g., a second timestep of 1,000,000 as compared to FIG. 5D).In FIG. 5E, the quick-play affordance 553 is currently selected (asindicated by the different manner of display).

In FIG. 5E, the XR environment 409 is defined by a fifth environmentstate and is associated with a fifth environment time. In variousimplementations, because the second timestep between the fourthenvironment time and the fifth environment time is so much larger thanthe first timestep between, e.g., the first environment time and thesecond environment time, the fifth environment state is generatedaccording to a second model, different than the first model, and basedon the fourth environment state.

In particular, rather than determining the fifth environment stateaccording to the application of the first model one million times, thefifth environment state is generated according to the application of thesecond model once.

In various implementations, the second model is more computationallyefficient than the first model. After such a long period of time,certain states of certain assets of the fourth environment state may notbe useful in determining the corresponding states of the fifthenvironment state. For example, whereas the location of the firstsquirrel 521 in the first environment state is used in the first modelto determine the location of the first squirrel 521 in the secondenvironment state, the location of the first squirrel 521 in the fourthenvironment state is not used to determine the location of the firstsquirrel 521 in the fifth environment state and, therefore, the secondmodel is more computationally efficient than the first model.

In various implementations, determining the fifth environment stateaccording to the second model includes determining a fifth age of thefirst tree 511 by adding the second timestep (e.g., 1,000,000) to thefourth age of the first tree 511, determining a fifth age of the secondtree 512 by adding the second timestep to the fourth age of the secondtree 512, determining a fifth age of the first squirrel 521 by addingthe second timestep to the fourth age of the first squirrel 521, anddetermining a fifth age of the second squirrel 522 by adding the secondtimestep to the fourth age of the second squirrel 522.

In various implementations, determining the fifth environment stateaccording to the second model includes determining a fifth location ofthe first tree 511 by copying the fourth location of the first tree 511and determining a fifth location of the second tree 512 by copying thefourth location of the second tree 512. Thus, the second model indicatesthat the first tree 511 and second tree 512 (e.g., assets having anasset type of “TREE”) do not change location.

In various implementations, determining the fifth environment stateaccording to the second model includes determining a fifth location ofthe first squirrel 521 and determining a fifth location of the secondsquirrel 522. Whereas, according to the first model, the fifth locationof the first squirrel 521 and the fifth location of the second squirrel522 are determined based on their respective fourth locations and fourthmotion vectors, in various implementations, the fifth location of thefirst squirrel 521 and the fifth location of the second squirrel 522 aredetermined independent of their respective fourth locations and fourthmotion vectors. For example, in various implementations, determining thefifth location of the first squirrel 521 and determining the fifthlocation of the second squirrel 522 includes randomly selectinglocations of the XR environment. In some embodiments, the respectivefifth locations are selected based on a uniform distribution over the XRenvironment. In some embodiments, the respective fifth locations areselected based on a probability distribution (which may be based on thepresence of other assets). For example, in some embodiments, the firstsquirrel 521 and second squirrel 522 are more likely to be locatedcloser to other assets.

As noted above, in various implementations, determining an environmentstate according to the first model includes determining motion vectorsof the first squirrel 521 and the second squirrel 522. However, as alsonoted above, in various implementations, the locations of the firstsquirrel 521 and the second squirrel 522 are determined independent oftheir respective motion vectors. Thus, in various implementations,determining the fifth environment state does not include determiningmotion vectors of the first squirrel 521 and the second squirrel 522. Invarious implementations, determining the fifth environment stateincludes determining the motion vectors as dummy or default values.Thus, in various implementations, determining the fifth environmentstate includes determining the motion vectors independent of theproximity of other assets to the location of the squirrels.

As noted above, in various implementations, determining an environmentstate according to the first model includes determining the locationand/or held state of an asset having an asset type of “ACORN.” Incontrast, in various implementations, determining the fifth environmentstate according to the second model includes removing assets of theasset type “ACORN” and potentially replacing them with new assets of theasset type “ACORN” based on the number of assets having the asset typeof “TREE” (and their respective probabilities of spawning an assethaving an asset type of “ACORN”).

Thus, in FIG. 5E, as compared to FIG. 5D, the first squirrel 521 and thesecond squirrel 522 have completely changed location, the second tree512 has grown taller to a third height 593, the first acorn 531 andsecond acorn 532 have disappeared, and a third acorn 533 has appeared.

FIG. 5F illustrates a portion of the display of the electronic device410 displaying a sixth image 500F of the representation of the scene 415including the XR environment 409 after another frame time. In FIG. 5F,the time indicator 540 indicates a current time of the XR environment409 of 2,000,004 (e.g., a timestep of 1,000,000 as compared to FIG. 5E).In FIG. 5F, the quick-play affordance 553 remains selected (as indicatedby the different manner of display).

In FIG. 5F, the XR environment 409 is defined by a sixth environmentstate and is associated with a sixth environment time. In variousimplementations, the sixth environment state is generated according tothe second model and based on the fifth environment state. In variousimplementations, determining the sixth environment state according tothe second model and based on the fifth environment state is performedas described above with respect to determining the fifth environmentstate according to the second model and based on the fourth environmentstate.

In FIG. 5F, as compared to FIG. 5E, the first squirrel 521 and secondsquirrel have completely changed location again, the second tree 512 hasagain grown taller to a fourth height 594, and a third acorn 533 hasdisappeared.

FIG. 5G illustrates a portion of the display of the electronic device410 displaying a seventh image 500G of the representation of the scene415 including the XR environment 409 in response to a user selection ofthe quicker-play affordance 553 after a frame time. In FIG. 5G, the timeindicator 540 indicates a current time of the XR environment 409 of10,002,000,004 (e.g., a third timestep of 10,000,000,000 as compared toFIG. 5F). In FIG. 5G, the quicker-play affordance 554 is currentlyselected (as indicated by the different manner of display).

In FIG. 5G, the XR environment 409 is defined by a seventh environmentstate and is associated with a seventh environment time. In variousimplementations, because the third timestep between the sixthenvironment time and the seventh environment time is so much larger thanthe second timestep between, e.g., the fourth environment time and thefifth environment time, the seventh environment state is generatedaccording to a third model, different than the second model, and basedon the sixth environment state.

In particular, rather than determining the seventh environment stateaccording to the application of the first model ten billion times (orthe second model ten thousand times), the fifth environment state isgenerated according to the application of the third model once.

In various implementations, the third model is more computationallyefficient than the second model. After such a long period of time,certain states of certain assets of the sixth environment state may notbe useful in determining the fifth environment state. For example,whereas the age of the second tree 512 in the fourth environment stateis used in the second model to determine the age of the second tree 512in the fifth environment state, the age and location of all assets isnot used by the third model. Rather, based on the number of trees,acorns, and squirrels, the third model generates new assets based anexpected number of trees, acorns, and squirrels at the seventhenvironment time and distributes them randomly in location and age.

In FIG. 5G, as compared to FIG. 5F, the first squirrel 521 and secondsquirrel 522 have been replaced by a third squirrel 523 and a fourthsquirrel 524; the first tree 511 and second tree 512 have been replacedby a third tree 513, a fourth tree 514, and a fifth tree 515; and afourth acorn 534 has appeared.

FIG. 5H illustrates a portion of the display of the electronic device410 displaying an eight image 500H of the representation of the scene415 including the XR environment 409 after another frame time. In FIG.5H, the time indicator 540 indicates a current time of the XRenvironment 409 of 20,002,000,004 (e.g., a third timestep of10,000,000,000 as compared to FIG. 5G). In FIG. 5H, the quicker-playaffordance 554 remains selected (as indicated by the different manner ofdisplay).

In FIG. 5H, the XR environment 409 is defined by an eighth environmentstate and is associated with an eighth environment time. In variousimplementations, the eighth environment state is generated according tothe third model and based on the seventh environment state. In variousimplementations, determining the eighth environment state according tothe third model and based on the seventh environment state is performedas described above with respect to determining the seventh environmentstate according to the third model and based on the sixth environmentstate.

In FIG. 5H, as compared to FIG. 5G, the third squirrel 523 and fourthsquirrel 524 have been replaced by a fifth squirrel 525, a sixthsquirrel 526, and a seventh squirrel 527; the third tree 513, the fourthtree 514, and the fifth tree 515 have been replaced by a sixth tree 516,a seventh tree 517, an eighth tree 518, and a ninth tree 519; and thefourth acorn 534 has been replaced with a fifth acorn 535 and a sixthacorn 536.

In various implementations, based on the number of trees, acorns, andsquirrels, rather than generating new assets based on an expected numberof trees, acorns, and squirrels at an environment time, the third modelgenerates an asset distribution indicating a likelihood of an assetbeing present at each location at the environment time. Displaying theasset distribution, e.g., as a cloud, represents a population density ofthe asset type at the environment time.

FIG. 5I illustrates a portion of the display of the electronic device410 displaying a ninth image 500I of the representation of the scene 415including the XR environment 409 with an asset distribution. In FIG. 5I,the time indicator 540 indicates a current time of the XR environment409 of 20,002,000,004 (e.g., a third timestep of 10,000,000,000 ascompared to FIG. 5G). In FIG. 5I, the quicker-play affordance 554remains selected (as indicated by the different manner of display).

In FIG. 5I, as compared to FIG. 5H, the fifth squirrel 525, the sixthsquirrel 526, and the seventh squirrel 527 are not displayed. Rather, asquirrel distribution 561 is displayed indicating the likelihood of asquirrel being present at respective locations at the eighth environmenttime.

FIG. 6 illustrates an environment state 600 in accordance with someimplementations. In various implementations, the environment state 600is a data object, such as an XML file. The environment state 600indicates inclusion in an XR environment of one or more assets andfurther indicates one or more states of the one or more assets.

The environment state 600 includes a time field 610 that indicates anenvironment time associated with the environment state.

The environment state 600 includes an assets field 620 including aplurality of individual asset fields 630 and 640 associated withrespective assets of the XR environment. Although FIG. 6 illustratesonly two assets, it is to be appreciated that the assets field 620 caninclude any number of asset fields.

The assets field 620 includes a first asset field 630. The first assetfield 630 includes a first asset identifier field 631 that includes anasset identifier of the first asset. In various implementations, theasset identifier includes a unique number. In various implementations,the asset identifier includes a name of the asset.

The first asset field 630 includes a first asset type field 632 thatincludes data indicating an asset type of the first asset. The firstasset field 630 includes an optional asset subtype field 633 thatincludes data indicating an asset subtype of the asset type of the firstasset.

The first asset field 630 includes a first asset states field 634including a plurality of first asset state fields 635A and 635B. Invarious implementations, the assets state field 634 is based on theasset type and/or asset subtype of the first asset. For example, whenthe asset type is “TREE”, the asset states field 634 includes an assetlocation field 635A including data indicating a location in the XRenvironment of the asset and an asset age field 635B including dataindicating an age of the asset. As another example, when the asset typeis “ANIMAL”, the asset states field 634 includes an asset motion vectorfield including data indicating a motion vector of the asset. As anotherexample, when the asset type is “ACORN”, the asset states field 634includes an asset held state field including data indicating which, ifany, other asset is holding the asset. As another example, when theasset type is “WEATHER”, the asset states field 634 includes an assettemperature field including data indicating a temperature of the XRenvironment, an asset humidity field including data indicating ahumidity of the XR environment, and/or an asset precipitation fieldincluding data indicating a precipitation condition of the XRenvironment.

The assets field 620 includes a second asset field 640. The second assetfield 640 includes a second asset identifier field 640 that includes anasset identifier of the second asset. The second asset field 630includes a second asset type field 642 that includes data indicating anasset type of the second asset. The second asset field 642 includes anoptional asset subtype field 643 that includes data indicating an assetsubtype of the asset type of the second asset.

The second asset field 640 includes a second asset states field 643including a plurality of second asset state fields 645A and 645B. Invarious implementations, the assets state field 644 is based on theasset type and/or asset subtype of the second asset.

FIG. 7 illustrates a block diagram of an environment state generator 750in accordance with some implementations. The environment state generator750 receives, as an input, a first environment state 710 associated witha first environment time and a timestep 715. The environment stategenerator 750 generates, as an output, a second environment state 720associated with a second environment time equal to the first environmenttime incremented by the timestep 715.

The environment state generator 750 includes a plurality of models752A-752C including a first model 752A, a second model 752B, and a thirdmodel 752C connected in parallel between a multiplexer 751 and ademultiplexer 753. The environment state generator 750 includes a modelselector 755 that controls the multiplexer 751 and the demultiplexer 753based on the timestep 715.

When the timestep 715 is a first value (or within a first range ofvalues), the model selector 755 controls the multiplexer 751 and thedemultiplexer 753 to feed the first environment state 710 and timestep715 to the first model 752A to generate the second environment state720. When the timestep 715 is a second value (or within a second rangeof values), the model selector 755 controls the multiplexer 751 and thedemultiplexer 753 to feed the first environment state 710 and thetimestep 715 to the second model 752B to generate the second environmentstate 720. When the timestep 715 is a third value (or within a thirdrange of values), the model selector 755 controls the multiplexer 751and the demultiplexer 753 to feed the first environment state 710 andthe timestep 715 to the third model 752C to generate the secondenvironment state 720.

Accordingly, each model 752A-752C, when selected by the model selector755, receives the first environment state 710 and the timestep 715 andgenerates the second environment state 720. In various implementations,each model 752A-752C includes respective heuristics and metadata used togenerate the second environment state 720.

FIG. 8 is a flowchart representation of a method 800 of generating anenvironment state of an environment in accordance with someimplementations. In various implementations, the method 800 is performedby a device with one or more processors, non-transitory memory, and acamera (e.g., the electronic device 120 of FIG. 3 or the electronicdevice 410 of FIG. 4). In some implementations, the method 800 isperformed by processing logic, including hardware, firmware, software,or a combination thereof (e.g., the environment state generator 750 ofFIG. 7). In some implementations, the method 800 is performed by aprocessor executing instructions (e.g., code) stored in a non-transitorycomputer-readable medium (e.g., a memory). Briefly, in somecircumstances, the method 800 includes obtaining a first environmentstate, associated with a first environment time, of an environment and atimestep, selecting a model from a plurality of models based on thetimestep, and applying the selected model to the first environment stateto generate a second environment state associated with a secondenvironment time.

The method 800 begins, in block 810, with the device obtaining a firstenvironment state, associated with a first environment time, of anenvironment, wherein the first environment state indicates inclusion inthe environment of one or more assets and further indicates one or morestates of the one or more assets. In various implementations, the method800 includes displaying the environment having the first environmentstate at a first time (e.g., a first real time).

In various implementations, the environment state is a data object, suchas an XML file. In various implementations, the first environment stateis manually programmed In various implementations, the first environmentstate is generated by applying a model to a previous environment state.

The method 800 continues, at block 820, with the device obtaining atimestep from the first environment time to a second environment time.In various implementations, the timestep is manually programmed Invarious implementations, the timestep is determined based on userinteraction with one or more timescale affordances respectivelyassociated with one or more timesteps.

The method 800 continues, at block 830, with the device selecting, basedon the timestep, a model from a plurality of different models. Invarious implementations, in response to obtaining a timestep having afirst value (or within a first range of values), the device selects afirst model and, in response to obtaining a timestep having a secondvalue (or within a second range of values), the device selects a secondmodel different from the first model. In various implementations, thesecond model is more computationally efficient than the first model.

The method 800 continues, at block 840, with the device determining, inaccordance with the selected model, a second environment state,associated with the second environment time, of the environment. Invarious implementations, the method 800 includes displaying theenvironment having the second environment state at a second time (e.g.,a second real time).

In various implementations, determining the second environment state inaccordance with the selected model (at block 840) includes, inaccordance with a selection (at block 830) of a first model, determininga second value of an asset state of the second environment state basedon a first value of the asset state of the first environment state. Forexample, as described above with respect to FIG. 5C, determining thethird environment state includes determining a third location of thefirst squirrel 521 based on the second location of the first squirrel521 and the second motion vector of the first squirrel 521. In variousimplementations, determining the second environment state in accordancewith the selected model (at block 840) includes, in accordance with aselection (at block 830) of a second model, determining a second valueof an asset state of the second environment state independent of thefirst value of the asset state of the first environment state. Forexample, as described above with respect to FIG. 5E, determining thefifth environment state includes determining a fifth location of thefirst squirrel 521 independent of the fourth location of the firstsquirrel 521 and the fourth motion vector of the first squirrel 521.

In various implementations, determining the second environment state inaccordance with the selected model (at block 840) includes, inaccordance with a selection (at block 830) of a first model, determininga second value of an asset state of the second environment state basedon the first environment state. For example, as described above withrespect to FIG. 5C, determining the third environment state includesdetermining a third motion vector of the first squirrel 521 based on theproximity of the third location of the first squirrel 521 to the thirdlocations of other assets (which are based on their second locations ofthe second environment state). In various implementations, determiningthe second environment state in accordance with the selected model (atblock 840) includes, in accordance with a selection (at block 830) of asecond model, forgoing determining the second value of the asset stateof the second environment state based on the first environment state.For example, as described above with respect to FIG. 5E, determining thefifth environment state does not include determining a fifth motionvector of the first squirrel 521.

In various implementations, determining the second environment state inaccordance with the selected model (at block 840) includes, inaccordance with a selection (at block 830) of a first model, determininga second value of an asset state of the second environment state basedon a first value of the asset state of the first environment state. Forexample, as described above with respect to FIG. 5E, determining thefifth environment state includes determining a fifth location of thefirst tree 511 based on the fourth location of the first tree 511. Invarious implementations, determining the second environment state inaccordance with the selected model (at block 840) includes, inaccordance with a selection (at block 830) of a second model, excludingthe asset having the asset state from the second environment state. Forexample, as described above with respect to FIG. 5G, determining theseventh environment state includes removing the first tree 511.

In various implementations, determining the second environment state inaccordance with the selected model (at block 840) includes, inaccordance with a selection (at block 830) of a second model, includingone or more new assets having the same asset type as the asset havingthe asset state based on the number of assets having a respective assettype in the first environment state. For example, as described abovewith respect to FIG. 5G, determining the seventh environment stateincludes adding the third tree 513, the fourth tree 514, and the fifthtree 515 based on the number of trees (and the number of squirrels) inthe sixth environment state. As another example, as described above withrespect to FIG. 5E, determining the fifth environment state includesadding the third acorn 533 based on the number of trees in the fifthenvironment state.

FIG. 9 is a flowchart representation of a method 900 of generatingenvironment states of an environment in accordance with someimplementations. In various implementations, the method 900 is performedby a device with one or more processors, non-transitory memory, and acamera (e.g., the electronic device 120 of FIG. 3 or the electronicdevice 410 of FIG. 4). In some implementations, the method 900 isperformed by processing logic, including hardware, firmware, software,or a combination thereof (e.g., the environment state generator 750 ofFIG. 7). In some implementations, the method 900 is performed by aprocessor executing instructions (e.g., code) stored in a non-transitorycomputer-readable medium (e.g., a memory). Briefly, in somecircumstances, the method 900 includes obtaining a first environmentstate, associated with a first environment time, of an XR environmentand a timestep, selecting a model from a plurality of models based onthe timestep, and applying the selected model to the first environmentstate to generate a second environment state associated with a secondenvironment time.

The method 900 begins, in block 910, with the device obtaining a firstenvironment state, associated with a first environment time, of anenvironment, wherein the first environment state indicates inclusion inthe environment of one or more assets and further indicates one or morestates of the one or more assets. For example, in variousimplementations, the first environment state includes data indicatingthe inclusion of a first asset of the one or more assets, a type of thefirst asset, a respective location of the first asset in theenvironment, and a respective age of the first asset. In variousimplementations, the method 900 includes displaying the environmenthaving the first environment state at a first time.

In various implementations, the environment state is a data object, suchas an XML file. In various implementations, the first environment stateis manually programmed In various implementations, the first environmentstate is generated by applying a model to a previous environment state.

The method 900 continues, at block 920, with the device determining,according to a first model and based on the first environment state, asecond environment state associated with a second environment time. Invarious implementations, the second environment state includes dataindicating the inclusion of a first asset of the one or more assets, thetype of the first asset, a respective location of the first asset in theenvironment, and a respective age of the first asset. In variousimplementations, the method 900 includes displaying the environmenthaving the second environment state at a second time (e.g., a frame timelater than the first time).

The method 900 continues, at block 930, with the device receiving aninput indicative of a timestep from the second environment time to athird environment time, wherein the timestep is different than adifference between the first environment time and the second environmenttime. In various implementations, receiving the input indicative of thetimestep includes receiving user input indicative of a selection of oneof a plurality of timescale affordances respectively associated with aplurality of timesteps. For example, in FIG. 5E, the user has selectedthe quick-play affordance 553 and the timestep between FIG. 5D and FIG.5E is different than the timestep between FIG. 5C and FIG. 5D.

The method 900 continues, at block 940, with the device determining,according to a second model, different than the first model, and basedon the second environment state, a third environment state associatedwith the third environment time. In various implementations, the secondmodel is more computationally efficient than the first model. In variousimplementations, the third environment state includes data indicatingthe inclusion of the first asset of the one or more assets, the type ofthe first asset, a respective location of the first asset in theenvironment, and a respective age of the first asset. In variousimplementations, the method 900 includes displaying the environmenthaving the third environment state at a third time (e.g., the frame timelater than the third time).

In various implementations, determining the second environment state inaccordance with the first model (at block 920) includes, determining asecond value of an asset state of the second environment state based ona first value of the asset state of the first environment state. Forexample, as described above with respect to FIG. 5C, determining thethird environment state includes determining a third location of thefirst squirrel 521 based on the second location of the first squirrel521 and the second motion vector of the first squirrel 521. In variousimplementations, determining the third environment state in accordancewith the second model (at block 940) includes determining a third valueof the asset state of the third environment state independent of thesecond value of the asset state of the second environment state. Forexample, as described above with respect to FIG. 5E, determining thefifth environment state includes determining a fifth location of thefirst squirrel 521 independent of the fourth location of the firstsquirrel 521 and the fourth motion vector of the first squirrel 521.

In various implementations, determining the second environment state inaccordance with the first model (at block 920) includes determining asecond value of an asset state of the second environment state based onthe first environment state. For example, as described above withrespect to FIG. 5C, determining the third environment state includesdetermining a third motion vector of the first squirrel 521 based on theproximity of the third location of the first squirrel 521 to the thirdlocations of other assets (which are based on their second locations ofthe second environment state). In various implementations, determiningthe second environment state in accordance with the second model (atblock 940) includes forgoing determining a third value of the assetstate of the third environment state based on the second environmentstate. For example, as described above with respect to FIG. 5E,determining the fifth environment state does not include determining afifth motion vector of the first squirrel 521.

In various implementations, determining the second environment state inaccordance with the first model (at block 920) includes determining asecond value of an asset state of the second environment state based ona first value of the asset state of the first environment state. Forexample, as described above with respect to FIG. 5E, determining thefifth environment state includes determining a fifth location of thefirst tree 511 based on the fourth location of the first tree 511. Invarious implementations, determining the third environment state inaccordance with the second model (at block 940) includes excluding theasset having the asset state from the third environment state. Forexample, as described above with respect to FIG. 5G, determining theseventh environment state includes removing the first tree 511.

In various implementations, determining the third environment state inaccordance with the second model (at block 940) includes including oneor more new assets having the same asset type as the asset having theasset state based on the number of assets having a respective asset typein the second environment state. For example, as described above withrespect to FIG. 5G, determining the seventh environment state includesadding the third tree 513, fourth tree 514, and fifth tree 515 based onthe number of trees (and the number of squirrels) in the sixthenvironment state. As another example, as described above with respectto FIG. 5E, determining the fifth environment state includes adding thethird acorn 533 based on the number of trees in the fifth environmentstate.

While various aspects of implementations within the scope of theappended claims are described above, it should be apparent that thevarious features of implementations described above may be embodied in awide variety of forms and that any specific structure and/or functiondescribed above is merely illustrative. Based on the present disclosureone skilled in the art should appreciate that an aspect described hereinmay be implemented independently of any other aspects and that two ormore of these aspects may be combined in various ways. For example, anapparatus may be implemented and/or a method may be practiced using anynumber of the aspects set forth herein. In addition, such an apparatusmay be implemented and/or such a method may be practiced using otherstructure and/or functionality in addition to or other than one or moreof the aspects set forth herein.

It will also be understood that, although the terms “first,” “second,”etc. may be used herein to describe various elements, these elementsshould not be limited by these terms. These terms are only used todistinguish one element from another. For example, a first node could betermed a second node, and, similarly, a second node could be termed afirst node, which changing the meaning of the description, so long asall occurrences of the “first node” are renamed consistently and alloccurrences of the “second node” are renamed consistently. The firstnode and the second node are both nodes, but they are not the same node.

The terminology used herein is for the purpose of describing particularimplementations only and is not intended to be limiting of the claims.As used in the description of the implementations and the appendedclaims, the singular forms “a,” “an,” and “the” are intended to includethe plural forms as well, unless the context clearly indicatesotherwise. It will also be understood that the term “and/or” as usedherein refers to and encompasses any and all possible combinations ofone or more of the associated listed items. It will be furtherunderstood that the terms “comprises” and/or “comprising,” when used inthis specification, specify the presence of stated features, integers,steps, operations, elements, and/or components, but do not preclude thepresence or addition of one or more other features, integers, steps,operations, elements, components, and/or groups thereof.

As used herein, the term “if” may be construed to mean “when” or “upon”or “in response to determining” or “in accordance with a determination”or “in response to detecting,” that a stated condition precedent istrue, depending on the context. Similarly, the phrase “if it isdetermined [that a stated condition precedent is true]” or “if [a statedcondition precedent is true]” or “when [a stated condition precedent istrue]” may be construed to mean “upon determining” or “in response todetermining” or “in accordance with a determination” or “upon detecting”or “in response to detecting” that the stated condition precedent istrue, depending on the context.

What is claimed is:
 1. A method comprising: at an electronic deviceincluding a processor and non-transitory memory: obtaining a firstenvironment state, associated with a first environment time, of anenvironment, wherein the first environment state indicates inclusion inthe environment of one or more assets and further indicates one or morestates of the one or more assets; determining, according to a firstmodel and based on the first environment state, a second environmentstate associated with a second environment time; receiving an inputindicative of a timestep from the second environment time to a thirdenvironment time, wherein the timestep is different than a differencebetween the first environment time and the second environment time;determining, according to a second model, different than the firstmodel, and based on the second environment state, a third environmentstate associated with the third environment time.
 2. The method of claim1, wherein the first environment state includes an XML file.
 3. Themethod of claim 1, wherein the first environment state, secondenvironment state, and third environment state include data indicating:the inclusion of a first asset of the one or more assets; a type of thefirst asset; a respective location of the first asset in theenvironment; and a respective age of the first asset.
 4. The method ofclaim 1, further comprising: displaying the environment having the firstenvironment state at a first time; displaying the environment having thesecond environment state at a second time a frame time later than thefirst time; and displaying the environment having the third environmentstate at a third time the frame time later than the second time.
 5. Themethod of claim 1, wherein receiving the input indicative of thetimestep includes receiving user input indicative of a selection of oneof a plurality of timescale affordances respectively associated with aplurality of timesteps.
 6. The method of claim 1, wherein the secondmodel is more computationally efficient than the first model.
 7. Themethod of claim 1, wherein: determining, in accordance with the firstmodel, the second environment state includes determining a second valueof an asset state of the second environment state based on a first valueof the asset state of the first environment state; and determining, inaccordance with the second model, the third environment state includesdetermining a third value of the asset state of the third environmentstate independent of the second value of the asset state of the secondenvironment state.
 8. The method of claim 1, wherein: determining, inaccordance with the first model, the second environment state includesdetermining a second value of an asset state of the second environmentstate based on the first environment state; and determining, inaccordance with the second model, the third environment state includesforgoing determining a third value of the asset state of the thirdenvironment state based on the second environment state.
 9. The methodof claim 1, wherein: determining, in accordance with the first model,the second environment state includes determining a second value of anasset state of the second environment state based on a first value ofthe asset state of the first environment state; and determining, inaccordance with the second model, the third environment state includesexcluding the asset having the asset state from the third environmentstate.
 10. The method of claim 9, wherein determining, in accordancewith the second model, the third environment state includes includingone or more new assets having the same asset type as the asset havingthe asset state based on a number of assets having a respective assettype in the second environment state.
 11. The method of claim 1, whereindetermining, in accordance with the second model, the third environmentstate includes determining a third value of an asset state of the thirdenvironment state by adding the timestep to a second value of the assetstate of the second environment state.
 12. A device comprising: anon-transitory memory; and one or more processors to: obtain a firstenvironment state, associated with a first environment time, of anenvironment, wherein the first environment state indicates inclusion inthe environment of one or more assets and further indicates one or morestates of the one or more assets; determine, according to a first modeland based on the first environment state, a second environment stateassociated with a second environment time; receive an input indicativeof a timestep from the second environment time to a third environmenttime, wherein the timestep is different than a difference between thefirst environment time and the second environment time; determine,according to a second model, different than the first model, and basedon the second environment state, a third environment state associatedwith the third environment time.
 13. The device of claim 12, wherein theone or more processors are to receive the input indicative of thetimestep by receiving user input indicative of a selection of one of aplurality of timescale affordances respectively associated with aplurality of timesteps.
 14. The device of claim 12, wherein the secondmodel is more computationally efficient than the first model.
 15. Thedevice of claim 12, wherein: the one or more processors are todetermine, in accordance with the first model, the second environmentstate by determining a second value of an asset state of the secondenvironment state based on a first value of the asset state of the firstenvironment state; and the one or more processors are to determine, inaccordance with the second model, the third environment state bydetermining a third value of the asset state of the third environmentstate independent of the second value of the asset state of the secondenvironment state.
 16. The device of claim 12, wherein: the one or moreprocessors are to determine, in accordance with the first model, thesecond environment state by determining a second value of an asset stateof the second environment state based on the first environment state;and the one or more processors are to determine, in accordance with thesecond model, the third environment state by forgoing determining athird value of the asset state of the third environment state based onthe second environment state.
 17. The device of claim 12, wherein: theone or more processors are to determine, in accordance with the firstmodel, the second environment state by determining a second value of anasset state of the second environment state based on a first value ofthe asset state of the first environment state; and the one or moreprocessors are to determine, in accordance with the second model, thethird environment state by excluding the asset having the asset statefrom the third environment state.
 18. The device of claim 17, whereinthe one or more processors are to determine, in accordance with thesecond model, the third environment state by including one or more newassets having the same asset type as the asset having the asset statebased on a number of assets having a respective asset type in the secondenvironment state.
 19. The device of claim 12, wherein the one or moreprocessors are to determine, in accordance with the second model, thethird environment state by determining a third value of an asset stateof the third environment state by adding the timestep to a second valueof the asset state of the second environment state.
 20. A non-transitorymemory storing one or more programs, which, when executed by one or moreprocessors of a device, cause the device to: obtain a first environmentstate, associated with a first environment time, of an environment,wherein the first environment state indicates inclusion in theenvironment of one or more assets and further indicates one or morestates of the one or more assets; determine, according to a first modeland based on the first environment state, a second environment stateassociated with a second environment time; receive an input indicativeof a timestep from the second environment time to a third environmenttime, wherein the timestep is different than a difference between thefirst environment time and the second environment time; determine,according to a second model, different than the first model, and basedon the second environment state, a third environment state associatedwith the third environment time.